code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Simple example of a training script."
      )
      parser.add_argument(
          "--dataset_name",
          type=str,
          default=None,
          help=(
              "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private,"
              " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem,"
              " or to a folder containing files that HF Datasets can understand."
          ),
      )
      parser.add_argument(
          "--dataset_config_name",
          type=str,
          default=None,
          help="The config of the Dataset, leave as None if there's only one config.",
      )
      parser.add_argument(
          "--model_config_name_or_path",
          type=str,
          default=None,
          help="The config of the UNet model to train, leave as None to use standard DDPM configuration.",
      )
      parser.add_argument(
          "--train_data_dir",
          type=str,
          default=None,
          help=(
              "A folder containing the training data. Folder contents must follow the structure described in"
              " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file"
              " must exist to provide the captions for the images. Ignored if `dataset_name` is specified."
          ),
      )
      parser.add_argument(
          "--output_dir",
          type=str,
          default="ddpm-model-64",
          help="The output directory where the model predictions and checkpoints will be written.",
      )
      parser.add_argument("--overwrite_output_dir", action="store_true")
      parser.add_argument(
          "--cache_dir",
          type=str,
          default=None,
          help="The directory where the downloaded models and datasets will be stored.",
      )
      parser.add_argument(
          "--resolution",
          type=int,
          default=64,
          help=(
              "The resolution for input images, all the images in the train/validation dataset will be resized to this"
              " resolution"
          ),
      )
      parser.add_argument(
          "--center_crop",
          default=False,
          action="store_true",
          help=(
              "Whether to center crop the input images to the resolution. If not set, the images will be randomly"
              " cropped. The images will be resized to the resolution first before cropping."
          ),
      )
      parser.add_argument(
          "--random_flip",
          default=False,
          action="store_true",
          help="whether to randomly flip images horizontally",
      )
      parser.add_argument(
          "--train_batch_size",
          type=int,
          default=16,
          help="Batch size (per device) for the training dataloader.",
      )
      parser.add_argument(
          "--eval_batch_size",
          type=int,
          default=16,
          help="The number of images to generate for evaluation.",
      )
      parser.add_argument(
          "--dataloader_num_workers",
          type=int,
          default=0,
          help=(
              "The number of subprocesses to use for data loading. 0 means that the data will be loaded in the main"
              " process."
          ),
      )
      parser.add_argument("--num_epochs", type=int, default=100)
      parser.add_argument(
          "--save_images_epochs",
          type=int,
          default=10,
          help="How often to save images during training.",
      )
      parser.add_argument(
          "--save_model_epochs",
          type=int,
          default=10,
          help="How often to save the model during training.",
      )
      parser.add_argument(
          "--gradient_accumulation_steps",
          type=int,
          default=1,
          help="Number of updates steps to accumulate before performing a backward/update pass.",
      )
      parser.add_argument(
          "--learning_rate",
          type=float,
          default=1e-4,
          help="Initial learning rate (after the potential warmup period) to use.",
      )
      parser.add_argument(
          "--lr_scheduler",
          type=str,
          default="cosine",
          help=(
              'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
              ' "constant", "constant_with_warmup"]'
          ),
      )
      parser.add_argument(
          "--lr_warmup_steps",
          type=int,
          default=500,
          help="Number of steps for the warmup in the lr scheduler.",
      )
      parser.add_argument(
          "--adam_beta1",
          type=float,
          default=0.95,
          help="The beta1 parameter for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_beta2",
          type=float,
          default=0.999,
          help="The beta2 parameter for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_weight_decay",
          type=float,
          default=1e-6,
          help="Weight decay magnitude for the Adam optimizer.",
      )
      parser.add_argument(
          "--adam_epsilon",
          type=float,
          default=1e-08,
          help="Epsilon value for the Adam optimizer.",
      )
      parser.add_argument(
          "--use_ema",
          action="store_true",
          help="Whether to use Exponential Moving Average for the final model weights.",
      )
      parser.add_argument(
          "--ema_inv_gamma",
          type=float,
          default=1.0,
          help="The inverse gamma value for the EMA decay.",
      )
      parser.add_argument(
          "--ema_power",
          type=float,
          default=3 / 4,
          help="The power value for the EMA decay.",
      )
      parser.add_argument(
          "--ema_max_decay",
          type=float,
          default=0.9999,
          help="The maximum decay magnitude for EMA.",
      )
      parser.add_argument(
          "--push_to_hub",
          action="store_true",
          help="Whether or not to push the model to the Hub.",
      )
      parser.add_argument(
          "--hub_token",
          type=str,
          default=None,
          help="The token to use to push to the Model Hub.",
      )
      parser.add_argument(
          "--hub_model_id",
          type=str,
          default=None,
          help="The name of the repository to keep in sync with the local `output_dir`.",
      )
      parser.add_argument(
          "--hub_private_repo",
          action="store_true",
          help="Whether or not to create a private repository.",
      )
      parser.add_argument(
          "--logger",
          type=str,
          default="tensorboard",
          choices=["tensorboard", "wandb"],
          help=(
              "Whether to use [tensorboard](https://www.tensorflow.org/tensorboard) or [wandb](https://www.wandb.ai)"
              " for experiment tracking and logging of model metrics and model checkpoints"
          ),
      )
      parser.add_argument(
          "--logging_dir",
          type=str,
          default="logs",
          help=(
              "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
              " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
          ),
      )
      parser.add_argument(
          "--local_rank",
          type=int,
          default=-1,
          help="For distributed training: local_rank",
      )
      parser.add_argument(
          "--mixed_precision",
          type=str,
          default="no",
          choices=["no", "fp16", "bf16"],
          help=(
              "Whether to use mixed precision. Choose"
              "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
              "and an Nvidia Ampere GPU."
          ),
      )
      parser.add_argument(
          "--prediction_type",
          type=str,
          default="epsilon",
          choices=["epsilon", "sample"],
          help="Whether the model should predict the 'epsilon'/noise error or directly the reconstructed image 'x0'.",
      )
      parser.add_argument("--ddpm_num_steps", type=int, default=1000)
      parser.add_argument(
          "--ddpm_num_inference_steps", type=int, default=1000
      )
      parser.add_argument(
          "--ddpm_beta_schedule", type=str, default="linear"
      )
      parser.add_argument(
          "--checkpointing_steps",
          type=int,
          default=500,
          help=(
              "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
              " training using `--resume_from_checkpoint`."
          ),
      )
      parser.add_argument(
          "--checkpoints_total_limit",
          type=int,
          default=None,
          help=(
              "Max number of checkpoints to store. Passed as `total_limit` to the `Accelerator` `ProjectConfiguration`."
              " See Accelerator::save_state https://huggingface.co/docs/accelerate/package_reference/accelerator#accelerate.Accelerator.save_state"
              " for more docs"
          ),
      )
      parser.add_argument(
          "--resume_from_checkpoint",
          type=str,
          default=None,
          help=(
              "Whether training should be resumed from a previous checkpoint. Use a path saved by"
              ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
          ),
      )
      parser.add_argument(
          "--enable_xformers_memory_efficient_attention",
          action="store_true",
          help="Whether or not to use xformers.",
      )
  
      args = parser.parse_args()
      env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
      if env_local_rank != -1 and env_local_rank != args.local_rank:
          args.local_rank = env_local_rank
  
      if args.dataset_name is None and args.train_data_dir is None:
          raise ValueError(
              "You must specify either a dataset name from the hub or a train data directory."
          )
  
      return args
  
imports: import argparse
  import inspect
  import logging
  import math
  import os
  from pathlib import Path
  import accelerate
  import datasets
  import torch
  import torch.nn.functional
  from accelerate import Accelerator
  from accelerate.logging import get_logger
  from accelerate.utils import ProjectConfiguration
  from datasets import load_dataset
  from huggingface_hub import create_repo, upload_folder
  from onnxruntime.training.optim.fp16_optimizer import FP16_Optimizer
  from onnxruntime.training.ortmodule import ORTModule
  from packaging import version
  from torchvision import transforms
  from tqdm.auto import tqdm
  import diffusers
  from diffusers import DDPMPipeline, DDPMScheduler, UNet2DModel
  from diffusers.optimization import get_scheduler
  from diffusers.training_utils import EMAModel
  from diffusers.utils import check_min_version, is_accelerate_version, is_tensorboard_available, is_wandb_available
  from diffusers.utils.import_utils import is_xformers_available
label_desc: Use image node_id 2 for calling a global function with example usage: parse_args() and returns: args